def fetch_risk_model(
self,
ref_date: str,
codes: Iterable[int],
risk_model: str = 'short',
excluded: Iterable[str] = None
) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_col = _map_risk_model_table(risk_model)
cov_risk_cols = [
risk_cov_table.__table__.columns[f] for f in total_risk_factors
]
query = select([risk_cov_table.FactorID, risk_cov_table.Factor] +
cov_risk_cols).where(
risk_cov_table.trade_date == ref_date)
risk_cov = pd.read_sql(query, self.engine).sort_values('FactorID')
if excluded:
risk_exposure_cols = [
FullFactor.__table__.columns[f] for f in total_risk_factors
if f not in set(excluded)
]
else:
risk_exposure_cols = [
FullFactor.__table__.columns[f] for f in total_risk_factors
]
query = select([FullFactor.code, special_risk_col] + risk_exposure_cols) \
.where(and_(FullFactor.trade_date == ref_date, FullFactor.code.in_(codes))).distinct()
risk_exp = pd.read_sql(query, self.engine)
return risk_cov, risk_exp
def fetch_risk_model_range(
self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
risk_model: str = 'short',
excluded: Iterable[str] = None
) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_col = _map_risk_model_table(risk_model)
cov_risk_cols = [
risk_cov_table.__table__.columns[f] for f in total_risk_factors
]
cond = risk_cov_table.trade_date.in_(
dates) if dates else risk_cov_table.trade_date.between(
start_date, end_date)
query = select([
risk_cov_table.trade_date, risk_cov_table.FactorID,
risk_cov_table.Factor
] + cov_risk_cols).where(cond)
risk_cov = pd.read_sql(query, self.engine).sort_values(
['trade_date', 'FactorID'])
if not excluded:
excluded = []
risk_exposure_cols = [
FullFactor.__table__.columns[f] for f in total_risk_factors
if f not in set(excluded)
]
cond = universe._query_statements(start_date, end_date, dates)
big_table = join(
FullFactor, UniverseTable,
and_(FullFactor.trade_date == UniverseTable.trade_date,
FullFactor.code == UniverseTable.code, cond))
query = select(
[FullFactor.trade_date, FullFactor.code, special_risk_col] + risk_exposure_cols).select_from(big_table) \
.distinct()
risk_exp = pd.read_sql(query, self.engine)
if universe.is_filtered:
codes = universe.query(self, start_date, end_date, dates)
risk_exp = pd.merge(risk_exp,
codes,
how='inner',
on=['trade_date', 'code'
]).sort_values(['trade_date', 'code'])
return risk_cov, risk_exp
def fetch_risk_model(
self,
ref_date: str,
codes: Iterable[int],
risk_model: str = 'short',
excluded: Iterable[str] = None,
model_type: str = None
) -> Union[FactorRiskModel, Tuple[pd.DataFrame, pd.DataFrame]]:
risk_cov_table, special_risk_table = _map_risk_model_table(risk_model)
cov_risk_cols = [
risk_cov_table.__table__.columns[f] for f in total_risk_factors
]
query = select([risk_cov_table.FactorID, risk_cov_table.Factor] +
cov_risk_cols).where(
risk_cov_table.trade_date == ref_date)
risk_cov = pd.read_sql(query, self.engine).sort_values('FactorID')
if excluded:
risk_exposure_cols = [
RiskExposure.__table__.columns[f] for f in total_risk_factors
if f not in set(excluded)
]
else:
risk_exposure_cols = [
RiskExposure.__table__.columns[f] for f in total_risk_factors
]
big_table = join(
RiskExposure, special_risk_table,
and_(RiskExposure.code == special_risk_table.code,
RiskExposure.trade_date == special_risk_table.trade_date))
query = select(
[RiskExposure.code.label("code"), special_risk_table.SRISK.label('srisk')] + risk_exposure_cols) \
.select_from(big_table).where(
and_(RiskExposure.trade_date == ref_date,
RiskExposure.code.in_(codes),
RiskExposure.flag == 1
))
risk_exp = pd.read_sql(
query, self.engine).dropna().drop_duplicates(subset=["code"])
if not model_type:
return risk_cov, risk_exp
elif model_type == 'factor':
factor_names = risk_cov.Factor.tolist()
new_risk_cov = risk_cov.set_index('Factor')
factor_cov = new_risk_cov.loc[factor_names, factor_names] / 10000.
new_risk_exp = risk_exp.set_index('code')
factor_loading = new_risk_exp.loc[:, factor_names]
idsync = new_risk_exp['srisk'] * new_risk_exp['srisk'] / 10000
return FactorRiskModel(factor_cov, factor_loading,
idsync), risk_cov, risk_exp
def fetch_risk_model_range(self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
risk_model: str = 'short',
excluded: Iterable[str] = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_table = _map_risk_model_table(risk_model)
cov_risk_cols = [risk_cov_table.__table__.columns[f] for f in total_risk_factors]
cond = risk_cov_table.trade_date.in_(dates) if dates else risk_cov_table.trade_date.between(start_date,
end_date)
query = select([risk_cov_table.trade_date,
risk_cov_table.FactorID,
risk_cov_table.Factor]
+ cov_risk_cols).where(
cond
)
risk_cov = pd.read_sql(query, self.engine).sort_values(['trade_date', 'FactorID'])
if not excluded:
excluded = []
risk_exposure_cols = [RiskExposure.__table__.columns[f] for f in total_risk_factors if f not in set(excluded)]
cond = universe._query_statements(start_date, end_date, dates)
big_table = join(RiskExposure, UniverseTable,
and_(
RiskExposure.trade_date == UniverseTable.trade_date,
RiskExposure.code == UniverseTable.code,
cond
)
)
big_table = join(special_risk_table,
big_table,
and_(
RiskExposure.code == special_risk_table.code,
RiskExposure.trade_date == special_risk_table.trade_date,
))
query = select(
[RiskExposure.trade_date,
RiskExposure.code,
special_risk_table.SRISK.label('srisk')] + risk_exposure_cols).select_from(big_table) \
.distinct()
risk_exp = pd.read_sql(query, self.engine).sort_values(['trade_date', 'code']).dropna()
return risk_cov, risk_exp
def fetch_risk_model(
self,
ref_date: str,
codes: Iterable[int],
risk_model: str = 'short',
excluded: Iterable[str] = None
) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_table = _map_risk_model_table(risk_model)
cov_risk_cols = [
risk_cov_table.__table__.columns[f] for f in total_risk_factors
]
query = select([risk_cov_table.FactorID, risk_cov_table.Factor] +
cov_risk_cols).where(
risk_cov_table.trade_date == ref_date)
risk_cov = pd.read_sql(query, self.engine).sort_values('FactorID')
if excluded:
risk_exposure_cols = [
RiskExposure.__table__.columns[f] for f in total_risk_factors
if f not in set(excluded)
]
else:
risk_exposure_cols = [
RiskExposure.__table__.columns[f] for f in total_risk_factors
]
big_table = join(
RiskExposure, special_risk_table,
and_(RiskExposure.code == special_risk_table.code,
RiskExposure.trade_date == special_risk_table.trade_date))
query = select([RiskExposure.code, special_risk_table.SRISK.label('srisk')] + risk_exposure_cols) \
.select_from(big_table).where(
and_(RiskExposure.trade_date == ref_date,
RiskExposure.code.in_(codes)
)).distinct()
risk_exp = pd.read_sql(query, self.engine).dropna()
return risk_cov, risk_exp.drop_duplicates(['code'])
def fetch_risk_model_range(
self,
universe: Universe,
start_date: str = None,
end_date: str = None,
dates: Iterable[str] = None,
risk_model: str = 'short',
excluded: Iterable[str] = None,
model_type: str = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
risk_cov_table, special_risk_table = _map_risk_model_table(risk_model)
cov_risk_cols = [
risk_cov_table.__table__.columns[f] for f in total_risk_factors
]
cond = risk_cov_table.trade_date.in_(
dates) if dates else risk_cov_table.trade_date.between(
start_date, end_date)
query = select([
risk_cov_table.trade_date, risk_cov_table.FactorID,
risk_cov_table.Factor
] + cov_risk_cols).where(cond)
risk_cov = pd.read_sql(query, self.engine).sort_values(
['trade_date', 'FactorID'])
risk_cov["trade_date"] = pd.to_datetime(risk_cov["trade_date"])
if not excluded:
excluded = []
risk_exposure_cols = [
RiskExposure.__table__.columns[f] for f in total_risk_factors
if f not in set(excluded)
]
cond = universe._query_statements(start_date, end_date, dates)
big_table = join(
RiskExposure, UniverseTable,
and_(RiskExposure.trade_date == UniverseTable.trade_date,
RiskExposure.code == UniverseTable.code,
RiskExposure.flag == 1, cond))
big_table = join(
special_risk_table, big_table,
and_(
RiskExposure.code == special_risk_table.code,
RiskExposure.trade_date == special_risk_table.trade_date,
))
query = select(
[RiskExposure.trade_date,
RiskExposure.code.label("code"),
special_risk_table.SRISK.label('srisk')] + risk_exposure_cols).select_from(big_table) \
.distinct()
risk_exp = pd.read_sql(query, self.engine).sort_values(['trade_date', 'code']) \
.dropna().drop_duplicates(["trade_date", "code"])
risk_exp["trade_date"] = pd.to_datetime(risk_exp["trade_date"])
if not model_type:
return risk_cov, risk_exp
elif model_type == 'factor':
new_risk_cov = risk_cov.set_index('Factor')
new_risk_exp = risk_exp.set_index('code')
risk_cov_groups = new_risk_cov.groupby('trade_date')
risk_exp_groups = new_risk_exp.groupby('trade_date')
models = dict()
for ref_date, cov_g in risk_cov_groups:
exp_g = risk_exp_groups.get_group(ref_date)
factor_names = cov_g.index.tolist()
factor_cov = cov_g.loc[factor_names, factor_names] / 10000.
factor_loading = exp_g.loc[:, factor_names]
idsync = exp_g['srisk'] * exp_g['srisk'] / 10000
models[ref_date] = FactorRiskModel(factor_cov, factor_loading,
idsync)
return pd.Series(models), risk_cov, risk_exp
